Natural Language Search for Real-Time Data Visualizations
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Solution Overview
Problem
Managing large enterprise networks with numerous assets and services is challenging due to the complexity of tracking performance, troubleshooting, and integrating updates, which involves reviewing vast amounts of telemetry data, often requiring labor-intensive and inaccurate searches for specific real-time data visualizations.
Innovation Solution
A system utilizing a large language model trained with natural language summaries of real-time data visualizations to quickly and accurately select relevant visualizations based on user queries, eliminating the need for image recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual search methods are used to find specific real-time data visualizations in large enterprise networks, then IT specialists can identify visualizations, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent introduces an image recognition system as an intermediary between the user's natural language query and the database of stored visualizations. The system automatically captures screenshots of visualizations, extracts features using image recognition algorithms, and matches them against user queries, eliminating the need for manual searching and significantly reducing time loss.
Solution Approach 2:
The patent replaces the mechanical manual search process with an automated computer vision system. Instead of IT specialists manually browsing and identifying visualizations, the system uses machine learning models to automatically analyze visualization images, extract meaningful features, and retrieve relevant results, thereby improving productivity and eliminating time waste.
2Measurement precision
If manual review of massive telemetry data is performed to track performance and troubleshoot, then comprehensive analysis can be achieved, but the process is time-consuming and prone to inaccuracies
Solution Approach 1:
The patent replaces manual data review with automated image recognition and natural language processing systems. The system automatically captures visualization data, processes it through AI models to extract performance metrics, and presents results in response to user queries, achieving both high accuracy and rapid processing without manual intervention.
Solution Approach 2:
The system enables self-service performance tracking by automatically monitoring enterprise services, capturing relevant visualizations, and making them searchable through natural language queries. IT specialists can directly query the system without manually reviewing data, and the system autonomously retrieves and presents the needed information, improving both accuracy and speed.
3Measurement precision
If comprehensive metadata including natural language summaries is generated for all visualizations, then search accuracy improves, but system complexity and processing requirements increase
Solution Approach 1:
The patent applies preliminary action by automatically generating metadata and natural language summaries for visualizations at the time they are created or stored, rather than processing them on-demand. This pre-processing approach improves search precision while managing system complexity through batch processing and efficient storage of pre-computed features.
Data Source
AI summary
Methods for allowing users to find relevant existing real-time data visualizations based on natural language rendering of configuration settings. Specifically, methods involve obtaining a natural language query related to performance of at least one enterprise service and selecting one or more real-time data visualizations from a plurality of existing real-time data visualizations by applying artificial intelligence or machine learning model to the natural language query. The methods further involve providing the one or more real-time data visualizations indicative of the performance of the at least one enterprise service.